Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
Animal Science
Non Technical Summary
Sustainability of beef production in the Southern United States is based upon breeding and genetic decisions and animal performance, especially with respect to reproduction and adaptation to local conditions. The work in this project will focus on 1) characterization of the inheritance and selection potential of various measures of cattle health and structural soundness, 2) evaluate cow longevity, fertility in conjunction with meat quality of their male progeny as a system, and 3) characterization of the inheritance and selective improvement of tolerance to ambient heat in beef cattle. These will include collection of novel traits (e.g., eye pigmentation as defense against solar-induced cancer; skull shape and interaction with health aspects; udder dimensions and failures; winter coat shedding dynamics,; cattle foot structure; sweating rate under heat duress), incorporation of a genomic level of investigation, and confluence of local environments across the Southern region of the United States. Economic components that correspond to genetic decision making will be generated and projected.
Animal Health Component
75%
Research Effort Categories
Basic
25%
Applied
75%
Developmental
(N/A)
Goals / Objectives
Estimate genetic variation associated with animal health and structural soundness using classical animal breeding and genomic techniques to facilitate sustainable beef cattle production systems
Systems approach to analyzing novel ERTs associated with female production including longevity, fertility and meat quality database creation
Documentation of genetic components and development of thermotolerance measurements pertaining to heat tolerance adaptive traits in sustainable beef cattle production systems.
Project Methods
Objectives 1 & 2:A general spreadsheet template for data collection will be created. Data collection in all stations will follow the general format of the template. Every time new data is collected, the excel file will be updated and a copy will be sent to the database manager. An R-script (To be developed by R. Rekaya and others) will be used to harness the new data (by comparison with previous stored files). The new information will be added to the general master data file. The master file as well as the excel files provided by the different stations will be available for download (as csv or tab delimited text files) from a secure server.Collected phenotypes will be a mixture of continuous (e.g., growth) and discrete (e.g., fertility) traits. Some confounding may exist between breed and location for some stations. Furthermore, systematic and random missingness is likely to occur for some traits. To face these challenges, sophisticated statistical tools will be employed. Mixed linear and threshold (latent variable) models will be used within frequentist and Bayesian frameworks. Existing tools such as ASReml 3.0 (Gilmour et al., 2009) and BLUPF90 suit (Misztal et al., 2002) will be used when appropriate. However, in the presence of multiple binary traits or/and a mixture between discrete and continuous responses, more specialized software will be used. Tools developed by the Georgia group (Rekaya et al., 2013; Chang et al., 2017) will be used in those cases.The following data will be collected for heifers and cows: (1) Breed of cow, (2) Sire ID/sire breed and dam ID/dam breed of cow, (3) cow birth date, (4) Mating information (natural or artificial insemination; single or multiple sires; number of cows per bull; season or insemination date(s), (5) Predominant forage in pastures (fescue 0 = no; 1 = yes), (6) Sire/sire breed of calf, (7) Cow:bull ratio, (8) Body condition score (date and stage of production), (9) Palpation status (0 = non-pregnant; 1= pregnant), (10) Calving status (0 = no; 1 = yes), (11) Weaning status ( 0= no; 1 = yes), (12) Calving date (calving season, spring or fall), (13) Calving difficulty (1 = normal; 2 = easy pull; 3 = hard pull; 4 = caesarian section; 5 = abnormal presentation, note the abnormal presentation of calf), (14) Calf vigor issues (1 = normal; 2 = weak but nursed without assistance; 3 = weak and assisted to nurse; add any notes), (15) Calf birth weight, (16) Calf weaning date, (17) Calf weaning weight, (18) Cow temperament at calving, (19) Date of death and reason/notes for cow or her calf, and (20) Date of culling and reason/notes for cow and/or her calf leaving herd.Economic analyses will be conducted to evaluate the value of traits measured from the cows and resulting calves. The economic value of selected traits will be analyzed using net present value methods. Simulation methods will be used to incorporate market risk and uncertainty into the analyses. These methods will allow quantification of economic impacts for numerous production considerations at the cow-calf level and assist in development of decision tools to aid in economic-based decision making; many breeding and genetic recommendations have not been formally evaluated economically.Net present value (NPV), the present value of the revenue minus costs over the investment period, is a common tool for analysis of the profitability of an investment. Cows are genuine investments, as revenues are generated from the calves sold and the salvage value of the cow.NPV=?_(i=1)^t?(REV-COST)/?(1+i)?^tNPV= net present valueREV= cash inflowsCOST = cash outflowsi= discount ratet = (cow age in years - 2)The NPV for each project year will be estimated for individual cows. The effect of increased longevity will be evaluated in terms of the difference in NPV. Stochastic simulation techniques, using Simetar (Richardson et al., 2008) will be used to account for variability in steer and heifer weaning weight and the weaning rate based on the variability observed in the cow data. Simulation provides the opportunity to make probabilistic estimates of alternative strategies based on the estimated distributions of economic returns.The basic cow-calf model will account for revenue and the cost associated with each cow in the herd. Revenue will be determined as products of the probability a cow would wean a steer with the stochastic weight of a steer and the stochastic price of a steer for that particular year. This value will then be added to the dollar amount generated by multiplying the probability of the cow having a heifer calf by the heifer price and stochastic heifer weight. Maintenance costs (e.g., $600, but revised in accordance with current conditions) will be deducted for each year that a cow is in the herd. The profit (loss) dollar amount will then be discounted to present value. This basic model will be altered relative to the different types of traits in the different objectives of the proposed project.Objective 3.1 Body Temperature and Sweating RateMultiparous cows will be utilized each year. Core body temperature will be measured for 96-hr periods during each trimester of pregnancy and during the postpartum period, prior to re-breeding. Temperature data loggers (Water Temperature Pro v2 Data Logger or TidbiT v2 Water Temperature Data Logger; Onset Computer Corp., Bourne, MA) attached to blank CIDR devices will be used to measure vaginal temperature (VT). Ambient conditions (temperature, relative humidity, solar radiation and wind speed) will be monitored using a weather station (Vantage Pro 2, Davis Instruments Corporation, Hayward, CA). Temperature-humidity index will be calculated using the formula THI = (0.8 x T) + [(RH/100) x (T - 14.4)] + 46.4, where T is the temperature (°C) and RH is the relative humidity (NOAA, 1976). Heat load index (HLI) will be calculated as proposed by Gaughan et al. (2010) for temperatures above 25º C: HLI = 8.62 + 0.38 × RH + 1.55 × BG - 0.5 × WS + e((2.4- WS)), with WS = wind speed (m/s) and e = base of the natural logarithm.Sweating rate (SR) will be evaluated using a VapoMeter (Delfin Technologies Ltd. Kuopio, Finland). On 3 d during each 96-hr evaluation period cows will be monitored for SR in the sun and shade and in the morning and afternoon. Cows will be allowed to adapt to the sun or shade conditions for 20 minutes prior to having SR measured. Measurements will be taken over the shoulder, the ribs and the flank on the left side of the animal. Respiration rate (RR) will be evaluated at these times by counting flank movements for 15 seconds and adjusting to breaths/minute (BPM).Data will be analyzed using stage of pregnancy (1st, 2nd, 3rd trimester and non-pregnant) as the main effects in the model.Objective 3.2 Shedding:Cow data will be collected for assessment of the influence of shedding type on production characters. These will include breed, breed type, and pedigree information on each animal for genetic analysis. Cow performance data will include cow weights, body condition scores, reproductive records and performance of their calves from birth to weaning. In order to appropriately build models, other information will be collected including forage type, calving season, internal parasite control, and type of mineral supplement.Analyses will be conducted using SAS (SAS Inst. Inc., Cary, NC) and ASReml (Gilmour et al., 2009). Because they are categorical rather than normally-distributed, hair scores will be analyzed using logistic regression procedures after application of appropriate link functions, such as a logit or probit function. Results will be presented after application of inverse of the link function used.